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Chapter Review - Paper 1
Assignment Due Date: April 11, 2022 at 11:59PM CST
Chapter 4: Training Models
1.
What Linear Regression training algorithm can you use if you have a training set with
millions of features?
For a training set with millions of features, you could use multiple linear
regression with LASSO regularization so that you may represent only the
most important features while maintaining accuracy on the training set.
2.
Suppose the features in your training set have very different scales. What algorithms might
suffer from this, and how? What can you do about it?
Ridge and LASSO regularized regressions will suffer from significant scale
disparities, and should integrate normalization before modeling, with
appropriate means of denormalization for the interpretation of results.
3. Can Gradient Descent get stuck in a local minimum when training a Logistic Regression model?
In logistic regression Gradient Descent always finds the global optimum
because the cost function used to model the decision boundary is convex.
4.
Do all Gradient Descent algorithms lead to...
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